



'''simple docstring'''



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Union[str, Any]						  =					(DEISMultistepScheduler,)
						_lowerCAmelCase  : Any						  =					(('num_inference_steps', 2_5),)







						def        __lowercase     (	self :   Dict					,     **lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {
													    '''num_train_timesteps''': 10_00,
													    '''beta_start''': 0.0001,
													    '''beta_end''': 0.02,
													    '''beta_schedule''': '''linear''',
													    '''solver_order''': 2,
													}

													config.update(**lowerCAmelCase__					)
													return config







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   Dict=0					,     **lowerCAmelCase__ :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      dict(self.forward_default_kwargs					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_sample
													SCREAMING_SNAKE_CASE       :   int         =      0.1 * sample
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      [residual + 0.2, residual + 0.15, residual + 0.10]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   str         =      self.get_scheduler_config(**lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   str         =      scheduler_class(**lowerCAmelCase__					)
																				scheduler.set_timesteps(lowerCAmelCase__					)
																				# copy over dummy past residuals
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler_class.from_pretrained(lowerCAmelCase__					)
																											new_scheduler.set_timesteps(lowerCAmelCase__					)
																											# copy over dummy past residuals
																											SCREAMING_SNAKE_CASE       :   str         =      dummy_past_residuals[: new_scheduler.config.solver_order]

																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      sample, sample
																				for t in range(lowerCAmelCase__					,     time_step + scheduler.config.solver_order + 1					):
																											SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      new_scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																											assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													pass







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   Optional[int]=0					,     **lowerCAmelCase__ :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      dict(self.forward_default_kwargs					)
													SCREAMING_SNAKE_CASE       :   Dict         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.dummy_sample
													SCREAMING_SNAKE_CASE       :   Any         =      0.1 * sample
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      [residual + 0.2, residual + 0.15, residual + 0.10]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   Any         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler_class(**lowerCAmelCase__					)
																				scheduler.set_timesteps(lowerCAmelCase__					)

																				# copy over dummy past residuals (must be after setting timesteps)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Any         =      scheduler_class.from_pretrained(lowerCAmelCase__					)
																											# copy over dummy past residuals
																											new_scheduler.set_timesteps(lowerCAmelCase__					)

																											# copy over dummy past residual (must be after setting timesteps)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      dummy_past_residuals[: new_scheduler.config.solver_order]

																				SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   int         =      new_scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   Optional[Any]=None					,     **lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													if scheduler is None:
																				SCREAMING_SNAKE_CASE       :   Any         =      self.scheduler_classes[0]
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      self.get_scheduler_config(**lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Dict         =      10
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.dummy_sample_deter
													scheduler.set_timesteps(lowerCAmelCase__					)

													for i, t in enumerate(scheduler.timesteps					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													return sample







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      dict(self.forward_default_kwargs					)

													SCREAMING_SNAKE_CASE       :   List[Any]         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   str         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   Dict         =      scheduler_class(**lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   List[str]         =      self.dummy_sample
																				SCREAMING_SNAKE_CASE       :   Dict         =      0.1 * sample

																				if num_inference_steps is not None and hasattr(lowerCAmelCase__					,     '''set_timesteps'''					):
																											scheduler.set_timesteps(lowerCAmelCase__					)
																				elif num_inference_steps is not None and not hasattr(lowerCAmelCase__					,     '''set_timesteps'''					):
																											SCREAMING_SNAKE_CASE       :   Dict         =      num_inference_steps

																				# copy over dummy past residuals (must be done after set_timesteps)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      [residual + 0.2, residual + 0.15, residual + 0.10]
																				SCREAMING_SNAKE_CASE       :   str         =      dummy_past_residuals[: scheduler.config.solver_order]

																				SCREAMING_SNAKE_CASE       :   Any         =      scheduler.timesteps[5]
																				SCREAMING_SNAKE_CASE       :   int         =      scheduler.timesteps[6]

																				SCREAMING_SNAKE_CASE       :   List[str]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   Any         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				self.assertEqual(output_a.shape					,     sample.shape					)
																				self.assertEqual(output_a.shape					,     output_a.shape					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# make sure that iterating over schedulers with same config names gives same results
													# for defaults
													SCREAMING_SNAKE_CASE       :   List[Any]         =      DEISMultistepScheduler(**self.get_scheduler_config()					)
													SCREAMING_SNAKE_CASE       :   str         =      self.full_loop(scheduler=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_mean.item() - 0.2_3916					) < 1e-3

													SCREAMING_SNAKE_CASE       :   List[Any]         =      DPMSolverSinglestepScheduler.from_config(scheduler.config					)
													SCREAMING_SNAKE_CASE       :   Dict         =      DPMSolverMultistepScheduler.from_config(scheduler.config					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      UniPCMultistepScheduler.from_config(scheduler.config					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      DEISMultistepScheduler.from_config(scheduler.config					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.full_loop(scheduler=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_mean.item() - 0.2_3916					) < 1e-3







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																				self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													self.check_over_configs(thresholding=lowerCAmelCase__					)
													for order in [1, 2, 3]:
																				for solver_type in ["logrho"]:
																											for threshold in [0.5, 1.0, 2.0]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    thresholding=lowerCAmelCase__					,     prediction_type=lowerCAmelCase__					,     sample_max_value=lowerCAmelCase__					,     algorithm_type='''deis'''					,     solver_order=lowerCAmelCase__					,     solver_type=lowerCAmelCase__					,     )







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													for algorithm_type in ["deis"]:
																				for solver_type in ["logrho"]:
																											for order in [1, 2, 3]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    solver_order=lowerCAmelCase__					,     solver_type=lowerCAmelCase__					,     prediction_type=lowerCAmelCase__					,     algorithm_type=lowerCAmelCase__					,     )
																																									SCREAMING_SNAKE_CASE       :   str         =      self.full_loop(
																																									    solver_order=lowerCAmelCase__					,     solver_type=lowerCAmelCase__					,     prediction_type=lowerCAmelCase__					,     algorithm_type=lowerCAmelCase__					,     )
																																									assert not torch.isnan(lowerCAmelCase__					).any(), "Samples have nan numbers"







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													self.check_over_configs(lower_order_final=lowerCAmelCase__					)
													self.check_over_configs(lower_order_final=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																				self.check_over_forward(num_inference_steps=lowerCAmelCase__					,     time_step=0					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.full_loop()
													SCREAMING_SNAKE_CASE       :   str         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_mean.item() - 0.2_3916					) < 1e-3







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.full_loop(prediction_type='''v_prediction'''					)
													SCREAMING_SNAKE_CASE       :   Any         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_mean.item() - 0.091					) < 1e-3







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   int         =      self.get_scheduler_config(thresholding=lowerCAmelCase__					,     dynamic_thresholding_ratio=0					)
													SCREAMING_SNAKE_CASE       :   Any         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      10
													SCREAMING_SNAKE_CASE       :   Dict         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   List[str]         =      self.dummy_sample_deter.half()
													scheduler.set_timesteps(lowerCAmelCase__					)

													for i, t in enumerate(scheduler.timesteps					):
																				SCREAMING_SNAKE_CASE       :   int         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   str         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													assert sample.dtype == torch.floataa









'''simple docstring'''



lowerCAmelCase_  :							List[Any]             =    [0, 2, 4, 6, 8]
lowerCAmelCase_  :							str             =    [1, 3, 5, 7, 9]
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					list[int]       ,		A	:					int					):
							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1       ,		-1       ,		-1					):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														SCREAMING_SNAKE_CASE       :   List[Any]         =      0
														for digit in range(10					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      digit
																					result += reversible_numbers(
																					    0       ,		(remainder + 2 * digit) // 10       ,		A       ,		A					)
														return result

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							for digita in range(10					):
														SCREAMING_SNAKE_CASE       :   int         =      digita

														if (remainder + digita) % 2 == 0:
																					SCREAMING_SNAKE_CASE       :   str         =      ODD_DIGITS
														else:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      EVEN_DIGITS

														for digita in other_parity_digits:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      digita
																					result += reversible_numbers(
																					    remaining_length - 2       ,		(remainder + digita + digita) // 10       ,		A       ,		A       ,		)
							return result
def        UpperCAmelCase (    A	:					int = 9					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
							for length in range(1       ,		max_power + 1					):
														result += reversible_numbers(A       ,		0       ,		[0] * length       ,		A					)
							return result


if __name__ == "__main__":
							print(f'{solution() = }')




